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머신러닝 기반 천연고무 자기유변고무의 전단계수 예측 KCI 등재

Machine Learning Approach for Predicting Shear modulus of Natural Rubber Magnetorheological Elastomers

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한국기계항공기술학회지(구 한국기계기술학회지) (Journal of the Korean Society of Mechanical and Aviation Technology)
한국기계기술학회 (Korean Society of Mechanical Technology)
초록

This study compares the shear behavior of anisotropic magnetorheological elastomers (MREs) using natural rubber (NR) and silicone rubber (Si) as matrices. The effects of magnetic flux density and compressive pre-stress on the shear modulus were experimentally investigated. Results showed that silicone-based MREs exhibited a 10–20% higher magnetorheological effect than NR-based ones due to stronger particle–matrix bonding and stable chain alignment under magnetic fields. In contrast, NR-based MREs showed greater stiffness variation under compressive stress, attributed to strain-hardening and volumetric constraint effects. These findings indicate that matrix selection significantly governs the magneto-mechanical response: silicone MREs are suitable for precision control and sensing, while NR MREs perform better in high-stress damping systems. This study provides fundamental insight for tailoring MREs according to design requirements.

목차
Abstract
1. 서 론
2. 이론적 배경(Theoretical Background)
    2.1 기존 모델링 접근법
    2.2 데이터 기반 접근법
3. 실험 데이터 및 전처리(Experimental Dataand Preprocessing)
    3.1 실험 구성 및 데이터 수집
    3.2 데이터 전처리 및 가공
    3.3 데이터 특성 분석
4. 머신러닝 기반 예측 결과
    4.1 Random Forest 모델 및 학습 결과
5. 결 론
References
저자
  • 정운창(School of ICT, Robotics & Mechanical Engineering, Hankyong National University) | Jeong Un-Chang Corresponding author